Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry

Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuousl...

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Main Author: Johnson, Jay Tillay
Published: Georgia Institute of Technology 2011
Subjects:
Online Access:http://hdl.handle.net/1853/37234
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spelling ndltd-GATECH-oai-smartech.gatech.edu-1853-372342013-01-07T20:37:09ZDefect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometryJohnson, Jay TillayNondestructive evaluationNondestructive testingDensitometryThin films DefectsComputer visionNondestructive testingQuick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.Georgia Institute of Technology2011-03-04T20:59:36Z2011-03-04T20:59:36Z2009-11-17Thesishttp://hdl.handle.net/1853/37234
collection NDLTD
sources NDLTD
topic Nondestructive evaluation
Nondestructive testing
Densitometry
Thin films Defects
Computer vision
Nondestructive testing
spellingShingle Nondestructive evaluation
Nondestructive testing
Densitometry
Thin films Defects
Computer vision
Nondestructive testing
Johnson, Jay Tillay
Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry
description Quick mass production of homogeneous thin film material is required in paper, plastic, fabric, and thin film industries. Due to the high feed rates and small thicknesses, machine vision and other nondestructive evaluation techniques are used to ensure consistent, defect-free material by continuously assessing post-production quality. One of the fastest growing inspection areas is for 0.5-500 micrometer thick thin films, which are used for semiconductor wafers, amorphous photovoltaics, optical films, plastics, and organic and inorganic membranes. As a demonstration application, a prototype roll-feed imaging system has been designed to inspect high-temperature polymer electrolyte membrane (PEM), used for fuel cells, after being die cast onto a moving transparent substrate. The inspection system continuously detects thin film defects and classifies them with a neural network into categories of holes, bubbles, thinning, and gels, with a 1.2% false alarm rate, 7.1% escape rate, and classification accuracy of 96.1%. In slot die casting processes, defect types are indicative of a misbalance in the mass flow rate and web speed; so, based on the classified defects, the inspection system informs the operator of corrective adjustments to these manufacturing parameters. Thickness uniformity is also critical to membrane functionality, so a real-time, full-field transmission densitometer has been created to measure the bi-directional thickness profile of the semi-transparent PEM between 25-400 micrometers. The local thickness of the 75 mm x 100 mm imaged area is determined by converting the optical density of the sample to thickness with the Beer-Lambert law. The PEM extinction coefficient is determined to be 1.4 D/mm and the average thickness error is found to be 4.7%. Finally, the defect inspection and thickness profilometry systems are compiled into a specially-designed graphical user interface for intuitive real-time operation and visualization.
author Johnson, Jay Tillay
author_facet Johnson, Jay Tillay
author_sort Johnson, Jay Tillay
title Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry
title_short Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry
title_full Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry
title_fullStr Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry
title_full_unstemmed Defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry
title_sort defect and thickness inspection system for cast thin films using machine vision and full-field transmission densitometry
publisher Georgia Institute of Technology
publishDate 2011
url http://hdl.handle.net/1853/37234
work_keys_str_mv AT johnsonjaytillay defectandthicknessinspectionsystemforcastthinfilmsusingmachinevisionandfullfieldtransmissiondensitometry
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